Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages

Abstract

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

“…Using a Bayesian statistical model that estimates relative growth rates per lineage, this study tracked the fraction of genomes from different lineages in each local authority, accounting for differences in local epidemiological dynamics including in the rate of introduction of different lineages. Using classic population genetics statistical tools, Mostefai et al [91] detected extensive population structure in viral genetic data from the first year of the pandemic, and characterized lineage expansion worldwide using changes in Tajima's D statistics [92] over time. Using birth-death processes, Scholer et al [93] were able to quantify the impact of interventions on the extinction probability of deleterious SARS-CoV-2 variants, which is applicable in the initial outbreak of a new variant of concern.…”

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data

“…In computational biology, dimensionality reduction techniques are commonly used to simplify the representation and analysis of complex datasets, like viral sequencing data, and to uncover inherent data biases. These techniques, which have seen significant improvements with the rise of high-dimensional data, include Principal Component Analysis (PCA) (Novembre et al 2008; Mostefai et al 2022), often used for summarizing human genetic data, t-SNE (Platzer 2013; Tamazian et al 2022) for analyzing local structures, and PHATE (Moon et al 2019), a novel method that allows visualization of both global and local structures in highdimensional data. Despite the potential of these methods, their application to the extensive SARS-CoV-2 data has been limited, often confined to analyzing consensus sequences (Hozumi et al 2021; B.…”

Population genetics in the early emergence of the Omicron SARS-CoV-2 variant in the provinces of South Africa

“…Population genetic approaches have been used to monitor the frequency of mutations, alternative alleles, lineage spread and population structure, and evolutionary dynamics of SARS-CoV-2 [14, 15]. These approaches have allowed for the detection of recurrent mutations and facilitated the visualization of genomic variation and lineage spread [14]. Previous population genetic studies of polymorphisms have revealed that SARS-CoV-2 genomes exhibit regional population structure in SARS-CoV-2 populations [13].…”